PYTHON — Analyzing Time And Space In Python

When it comes to analyzing the performance of an algorithm, we often talk about its time and space complexity. Time complexity refers to how long an algorithm takes to run, while space complexity is the amount of computer memory it needs to work. In computer science, we typically use Big O notation to represent the worst-case runtime of an algorithm as a function of the size of the input, denoted as “n”. For example, linear search has a time complexity of O(n) because, in the worst case, it may take “n” comparison operations to find the desired element in a list of “n” items. <a href="https://medium.com/paulacy-pulse/python-analyzing-time-and-space-in-python-49c82fe56d8c">Click Here</a>